Can suicide risk be predicted to plan for prevention?
Authors: Michelle Degli Esposti, Elinore J Kaufman
Journal: The Lancet Public Health (2023)

Suicide is a leading cause of death in the USA, and between 2000 and 2018, rates increased by 36% to 14·5 deaths per 100 000 people.1Centers for Disease Control and PreventionSuicide data and statistics.https://www.cdc.gov/suicide/suicide-data-statistics.htmlDate accessed: November 7, 2022Google Scholar Although this trend is pervasive, risk is not distributed evenly across the country or the population. 80% of deaths by suicide occur in men, and rates are highest among Native Americans and White Americans, with 29·4 suicide deaths per 100 000 occurring among White men aged 65 years and older. Conversely, suicide rates have decreased by 5% since 2018 among White individuals, but have continued to increase in other groups, including among young Black men.2Coleman BW Ring the Alarm. The crisis of Black youth suicide in America.https://watsoncoleman.house.gov/imo/media/doc/full_taskforce_report.pdfDate accessed: April 18, 2021Google Scholar Beyond demographics, individual risk factors for suicide include violent victimisation, major depressive disorder, chronic pain, substance abuse, and financial stress.3Centers for Disease Control and PreventionRisk and protective factors.https://www.cdc.gov/suicide/factors/index.htmlDate accessed: November 8, 2022Google Scholar Situational and community-level factors such as unemployment rates and accessibility of lethal means, particularly firearms, can also increase the risk of suicide.4Milner A Hjelmeland H Arensman E Leo DD Social-environmental factors and suicide mortality: a narrative review of over 200 articles.Sociol Mind. 2013; 3: 137-148Crossref Google Scholar Individual-level models incorporating risk factors can classify people as being at high or low risk of death by suicide, but the predictive accuracy of these models is close to zero, making targeted prevention efforts challenging.5Belsher BE Smolenski DJ Pruitt LD et al.Prediction models for suicide attempts and deaths: a systematic review and simulation.JAMA Psychiatry. 2019; 76: 642-651Crossref PubMed Scopus (274) Google Scholar If individual-level models alone cannot provide the information necessary to prevent death by suicide, population-level models might be crucial to identifying areas of concern and allocating resources appropriately. In The Lancet Public Health, Sasikiran Kandula and colleagues use geospatial modelling to estimate county-level suicide risk, generating accurate predictions that have the potential to inform future intervention.6Kandula S Martinez-Alés G Rutherford C et al.County-level estimates of suicide mortality in the USA: a modelling study.Lancet Public Health. 2023; (published online Jan 23.)https://doi.org/10.1016/S2468-2667(22)00290-0Summary Full Text Full Text PDF PubMed Scopus (2) Google Scholar By taking into account spatial and temporal autocorrelation, this approach allows the identification of high-risk counties and identifies areas of focus for intervention. Suicide risk was estimated to increase with each SD increase in firearm ownership (2·8% [95% credible interval (CrI) 1·8 to 3·9]), prevalence of major depressive episode (1·0% [0·4 to 1·5]), and unemployment rate (2·8% [1·9 to 3·8]). Conversely, risk was estimated to decrease by 4·3% (–5·1 to –3·2) for each SD increase in median household income and by 4·3% (–5·8 to –2·5) for each SD increase in population density. This useful analysis yields several important follow-up questions. First, a small set of covariates representing known risk factors for suicide were included in the models. Additional important contributors are well documented, and including these could improve model performance and usefulness.7Mukherjee S Wei Z Suicide disparities across metropolitan areas in the US: a comparative assessment of socio-environmental factors using a data-driven predictive approach.PLoS One. 2021; 16e0258824Crossref Scopus (5) Google Scholar In particular, no county-level metric of education or access to mental health care was included in their approach. Additionally, state-level rates of major depressive disorder and firearm ownership were used to estimate county-level rates. Although this decision might reflect low data availability, both of these factors vary substantially within states, further hindering model performance and interpretation. Data availability is a pervasive issue and might represent one of the key obstacles in the optimisation and dissemination of population risk prediction models for suicide. Second, a large proportion of US counties are rural, with relatively small populations. In these counties, small changes in absolute numbers of deaths translate to large changes in death rates at the population level, making modelling particularly challenging, as we have previously described.8Degli Esposti M Gravel J Kaufman EJ Delgado MK Richmond TS Wiebe DJ County-level variation in changes in firearm mortality rates across the US, 1989 to 1993 vs 2015 to 2019.JAMA Network Open. 2022; 5e2215557Crossref Scopus (5) Google Scholar Kandula and colleagues' results also echo the concerning finding that there are major disparities in suicide across US counties, which seem to be widening over time.8Degli Esposti M Gravel J Kaufman EJ Delgado MK Richmond TS Wiebe DJ County-level variation in changes in firearm mortality rates across the US, 1989 to 1993 vs 2015 to 2019.JAMA Network Open. 2022; 5e2215557Crossref Scopus (5) Google Scholar, 9Miller M Azrael D Barber C Suicide mortality in the United States: the importance of attending to method in understanding population-level disparities in the burden of suicide.Annu Rev Public Health. 2012; 33: 393-408Crossref PubMed Scopus (164) Google Scholar Their geospatial models were constructed retrospectively, but they hold promise for prospective predictions and tackling high-risk areas. Suicide rates remain high and, in some counties and subgroups, are continuing to rise, thus the use of up-to-date data to monitor risk in real time could inform needed interventions. Moreover, geospatial approaches have the potential to identify new opportunities for innovation by highlighting areas that stand out or by focusing on unusual improving or worsening trends that could identify new risks or protective factors and opportunities for innovation.8Degli Esposti M Gravel J Kaufman EJ Delgado MK Richmond TS Wiebe DJ County-level variation in changes in firearm mortality rates across the US, 1989 to 1993 vs 2015 to 2019.JAMA Network Open. 2022; 5e2215557Crossref Scopus (5) Google Scholar This geospatial analysis adds to growing evidence of the importance of population risk modelling and its actionable role in informing public health strategies. Although the question of what county-level interventions might be most effective at reducing suicides was beyond the scope of this paper, interventions might include partnering with local health departments10Bommersbach TJ Rosenheck RA Everett AS Suicide hot spots: leveraging county-level data and local agencies to target prevention in high-risk areas.Public Health Rep. 2022; 137: 408-413Crossref PubMed Scopus (2) Google Scholar to improve mental health care access, decreasing local access to lethal means (eg, firearms), or increasing focused interventions in crisis hotline services, primary care clinics, and emergency departments.11Jacovides CL Karnick AT Whitehorn GL Kaufman EJ Suicide in American cities.Curr Trauma Rep. 2021; 7: 115-124Crossref Scopus (1) Google Scholar Location might also be relevant at an even more local level, since prevention initiatives targeting suicide hotspots, such as known jumping sites and isolated locations, have been shown to be effective.12Pirkis J Too LS Spittal MJ Krysinska K Robinson J Cheung YTD Interventions to reduce suicides at suicide hotspots: a systematic review and meta-analysis.Lancet Psychiatry. 2015; 2: 994-1001Summary Full Text Full Text PDF PubMed Scopus (101) Google Scholar Ultimately, population-level interventions that target the social, economic, and physical environment and individual-level interventions based on rich data collected in real time are needed to decrease risk across the population. We declare no competing interests. County-level estimates of suicide mortality in the USA: a modelling studyArea-level characteristics and the conditional autoregressive models can estimate population-level suicide risk. Availability of near real-time situational data are necessary for the translation of these models into a surveillance setting. Monitoring changes in population-level risk of suicide could help public health agencies select and deploy targeted interventions quickly. Full-Text PDF Open Access